About:
The Accord.NET Framework is a .NET machine learning framework combined with audio and image processing libraries completely written in C#. It is a complete framework for building production-grade computer vision, computer audition, signal processing and statistics applications even for commercial use. A comprehensive set of sample applications provide a fast start to get up and running quickly, and an extensive online documentation helps fill in the details.

Other additions include new statistical hypothesis tests such as Anderson-Daring and Shapiro-Wilk; as well as support for all of LIBLINEAR's support vector machine algorithms; and format reading support for MATLAB/Octave matrices, LibSVM models, sparse LibSVM data files, and many others.

For a complete list of changes, please see the full release notes at the release details page at:

About:
Investigation of dependencies between multiple data sources allows the
discovery of regularities and interactions that are not seen in
individual data sets. The demand for such methods is increasing with
the availability and size of co-occurring observations in
computational biology, open data initiatives, and in other domains. We
provide practical, open access implementations of general-purpose
algorithms that help to realize the full potential of these
information sources.